merge isinstance calls (#6689)

You can pass a tuple of types you want to check as the second argument
to `isinstance`. If the object matches with any of the types, it will
return `True` else `False`.

---------

Co-authored-by: deepsource-autofix[bot] <62050782+deepsource-autofix[bot]@users.noreply.github.com>

Author: deepsource-autofix[bot]

monai/apps/nnunet/nnunetv2_runner.py

@@ -521,7 +521,7 @@ def train_single_model(self, config: Any, fold: int, gpu_id: tuple | list | int
             kwargs.pop("export_validation_probabilities")
             logger.warning("please specify the `export_validation_probabilities` in the __init__ of `nnUNetV2Runner`.")
 
-        if isinstance(gpu_id, tuple) or isinstance(gpu_id, list):
+        if isinstance(gpu_id, (tuple, list)):
             if len(gpu_id) > 1:
                 gpu_ids_str = ""
                 for _i in range(len(gpu_id)):

monai/handlers/garbage_collector.py

@@ -49,7 +49,7 @@ class GarbageCollector:
 
     def __init__(self, trigger_event: str | Events | CallableEventWithFilter = "epoch", log_level: int = 10):
         self.trigger_event: Events | CallableEventWithFilter
-        if isinstance(trigger_event, Events) or isinstance(trigger_event, CallableEventWithFilter):
+        if isinstance(trigger_event, (Events, CallableEventWithFilter)):
             self.trigger_event = trigger_event
         elif trigger_event.lower() == "epoch":
             self.trigger_event = Events.EPOCH_COMPLETED

tests/test_focal_loss.py

@@ -110,7 +110,6 @@ def test_consistency_with_cross_entropy_2d(self):
 
     def test_consistency_with_cross_entropy_2d_no_reduction(self):
         """For gamma=0 the focal loss reduces to the cross entropy loss"""
-        import numpy as np
 
         focal_loss = FocalLoss(to_onehot_y=False, gamma=0.0, reduction="none", weight=1.0)
         ce = nn.BCEWithLogitsLoss(reduction="none")

tests/test_grid_patch.py

@@ -109,7 +109,7 @@ def test_grid_patch(self, in_type, input_parameters, image, expected):
                 output_patch,
                 in_type(expected_patch),
                 type_test=False,
-                device_test=True if isinstance(in_type(expected_patch), torch.Tensor) else False,
+                device_test=bool(isinstance(in_type(expected_patch), torch.Tensor)),
             )
 
     @parameterized.expand([TEST_CASE_META_0, TEST_CASE_META_1])

tests/test_grid_patchd.py

@@ -94,7 +94,7 @@ def test_grid_patchd(self, in_type, input_parameters, image_dict, expected):
                 output_patch,
                 in_type(expected_patch),
                 type_test=False,
-                device_test=True if isinstance(in_type(expected_patch), torch.Tensor) else False,
+                device_test=bool(isinstance(in_type(expected_patch), torch.Tensor)),
             )
 
 

tests/test_rand_grid_patch.py

@@ -114,7 +114,7 @@ def test_rand_grid_patch(self, in_type, input_parameters, image, expected):
                 output_patch,
                 in_type(expected_patch),
                 type_test=False,
-                device_test=True if isinstance(in_type(expected_patch), torch.Tensor) else False,
+                device_test=bool(isinstance(in_type(expected_patch), torch.Tensor)),
             )
 
     @parameterized.expand([TEST_CASE_META_0, TEST_CASE_META_1])

tests/test_rand_grid_patchd.py

@@ -99,7 +99,7 @@ def test_rand_grid_patchd(self, in_type, input_parameters, image_dict, expected)
                 output_patch,
                 in_type(expected_patch),
                 type_test=False,
-                device_test=True if isinstance(in_type(expected_patch), torch.Tensor) else False,
+                device_test=bool(isinstance(in_type(expected_patch), torch.Tensor)),
             )